In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks or unrealistic assumptions being made in urban-air-quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution and the large uncertainties associated with measurements of low-cost sensors. This study presents a practical approach to producing high-spatiotemporal-resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open-source atmospheric-dispersion model and emission proxies from open-data sources, and (2) a practical spatial-interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO₂) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy (defined as the ratio between the root mean square error and the mean of the observations) of 39 % within 2 km of an observation location and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. It is shown that during the summer holiday period, NO₂ concentrations drop about 10 %. The reduction is less in the historic city centre, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, it is shown that Retina can be applied for an enhanced understanding of reference measurements and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

中文翻译：

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异质观测对城市空气质量的高分辨率制图：一种新方法及其在阿姆斯特丹的应用

在世界上许多城市，人们都面临着更高水平的空气污染。由于官方监测网络的稀疏性或在城市空气质量模型中做出的不切实际的假设，当地空气质量通常并不为人所知。近年来已经可以使用的低成本传感器技术具有提供补充信息的潜力。不幸的是，由于空气污染的局部性以及与低成本传感器的测量相关的巨大不确定性，基于不同来源对城市空气污染的综合解释并不简单。这项研究提供了一种实用的方法来生成城市空气污染的高时空分辨率地图，该地图能够吸收来自异构数据流的空气质量数据。它提供了两个步骤的解决方案：（1）建立一个通用的空气质量模型，该模型由开放源大气弥散模型和来自开放数据源的排放代理驱动；（2）一个实用的空间插值方案，能够同化具有不同精度的观测值。该方法被称为视网膜，已被应用并评估了二氧化氮（2016年夏天，在荷兰阿姆斯特丹的NO ₂）。参考测量值的同化产生了小时图，其典型精度（定义为均方根误差与观测值的平均值之比）在39％以内2公里的观察点，距离较大的地方为53％。如果包括对Urban AirQ活动的低成本测量，这些地图将揭示出官方网络欠采样区域的更详细的集中模式。结果表明，暑假期间NO ₂ 浓度下降约10％。在历史悠久的市中心，减少的幅度较小，而在连接城市北部和南部的隧道的出入通道周围发现的减少幅度最大，该隧道已关闭以进行维护。不断变化的集中模式表明交通流量如何重定向到其他主要道路。总体而言，已表明视网膜可用于增强对参考测量的了解，并可作为将低成本测量与参考测量相集成的框架，以便在市区获得更好的本地化信息。